Interpretable Probabilistic Divisive Clustering of Large Node-Attributed Networks

Abstract

Clustering of social networks, known as community detection is a fundamental part of social network analysis. A community (also known as a module or cluster) is a set of nodes grouped together according to some characteristic. Traditionally, a community has been thought of as a set of nodes that are more densely connected with each other than the rest of the network. Introducing node attributes to a social network, allows for two possible sources of information when clustering the network: The network structure, and the attributes describing the nodes. Traditional community detection methods supporting both these sources of information tend to be computationally complex and the resulting clusters are difficult to interpret in the sense of what characteristic they were grouped on. We present two methods (probabilistic divisive clustering and top-sampled community search) built on-top of already existing community detection methods (CESNA and FocusCO). Both of our methods aim to detect communities with a specified attribute association, yielding interpretable results in a feasible amount of time. The community detection algorithms our methods are built upon, are applied to different datasets in order to examine the runtime performance. We also display how our proposed methods can be used to detect communities formed around topics of interest and how a network can iteratively be clustered in order to detect subcommunities with a specified attribute association. In conjunction with research about psychological profiling, we believe that our proposed methods could be used to detect communities of people having similar psychological profile in online social networks.